Similar to layer pruning, here as well we incentivise the network to reduce its depth by completely eliminating a particular branch. The type of pruning can be beneficial in networks with multiple branches such as within inception block [1]} or in those networks that use ensembles of multiple parallel branches [2]}. Here again we propose two modifications, first, we developed an algorithm that introduces shortcut connections after a branched out sequence of layers if branches are not already present in the network. Secondly we extend the idea of filter pruning and layer pruning for branch pruning by introducing a learnable branch dropout parameter \(\phi ^r_l\) . This pruning parameter is multiplied with the whole branch. Let a branch is defined as \(\mathbf {r}_{l-1} = \mathbf {h}_{l-n}:\mathbf {h}_{l-1}\) , so the new equations for any output would be:
\(\mathbf {h}_l = f(\mathbf {r}_{l-1})g(\Phi _{l}^r,\epsilon ))+\mathbf {h}_{l-n}.\) 
